Systems and Methods to Provide Search Based on Social Graphs and Affinity Groups

ABSTRACT

Methods, machine-readable media, apparatuses and systems are provided to identify and/or present information based on relationship-based recommendations. The information may be search results; and the relationship-based recommendations may be recommendations or preferences specified by related people in one or more social networks or affinity groups. For example, the search results can be presented in an order based at least in part on the recommendations and the relationship between the people who made the recommendations and the person who requested the search results.

BACKGROUND

1. Field

At least some embodiments of the disclosure relate generally to thefield of information searches and, more particularly but not limited to,identifying and presenting relationship-based recommendations forsearches.

2. Description of the Related Art

Some search tools allow a user to search using a search query. Forexample, a user may enter a location and a query for “Italianrestaurants” to identify Italian restaurants in a specified area.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages will become betterunderstood with regard to the following description, appended claims,and accompanying drawings where:

FIG. 1 is a diagram of one embodiment of a system for performing asearch method.

FIG. 2 is a diagram of one embodiment of a relationship graph.

FIG. 3 is a flow chart illustrating one embodiment of a method of asearch method.

FIG. 4 illustrates a screen display of one embodiment of a searchinterface.

FIG. 5 illustrates a screen display of another embodiment of a searchinterface.

FIG. 6 is a diagrammatic representation of an embodiment of a machine,within which a set of instructions for causing the machine to performone or more of the methodologies discussed herein may be executed.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well known or conventional details are not described in orderto avoid obscuring the description. References to one or an embodimentin the present disclosure can be, but not necessarily are, references tothe same embodiment; and, such references mean at least one.

Reference in this specification to “one embodiment” or an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the disclosure. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but not other embodiments.

Embodiments of the disclosure includes methods, machine-readable media,apparatuses and systems to identify and/or present information based onrelationship-based recommendations. The information may be searchresults; and the relationship-based recommendations may berecommendations or preferences specified by related people in one ormore social networks or affinity groups. For example, the search resultscan be presented in an order based at least in part on therecommendations and the relationship between the people who made therecommendations and the person who requested the search results.

FIG. 1 is a diagram of one embodiment of a system for performing asearch method.

In some embodiments, a search device (e.g., 100) aggregatesrecommendations of businesses, service providers, products, information,entertainment programs, merchants, etc., on one or more social networks(e.g., 120, 130, etc.), tracks the user identifications of those makingthe recommendations on the social networks (e.g., 120, 130, etc.) andretrieves the relationships between user identifications from the socialnetworks. The search device also provides search results in response toa searching user's search query. The search device selects businessesthat are most relevant based on the search query and the number ofrecommendations of each business from users that are most closelyrelated to the searching user. In some embodiments, the search devicecounts only the number of recommending users of a predetermined degreeof closeness to the searching user. The searching user can use the rankof the business in the search result, the number of recommending usersand the identity of some or all of the recommending users to influencetheir decision as to which business to use. Generally speaking, therecommendations of more closely related recommending users are morerelevant to a searching user than recommendations of more distantlyrelated or unrelated users.

In FIG. 1, people use user devices (e.g., 140, 150) to connect toservices through the internet (110). User devices (e.g., 140, 150) cantake many forms including desktop and notebook computers, mobile phones,and personal digital assistants (PDAs). In the illustrated embodiment,the user device 140 and the user device 150 are configured to beconnected through the internet 110 to the social network 120, the socialnetwork 130 and the search device 100.

The social network 120 and the social network 130 may take variousforms. Social networks include online communities where users interactand establish relationships with each other. Examples of social networksinclude the FACEBOOK®, MySpace® and Linkedin® web sites. (FACEBOOK,MySpace and Linkedin are registered trademarks of Facebook, Inc.,Myspace, Inc. and Linkedin Corporation, respectively.) Users of socialnetworks (e.g., 140, 150) interact with each other in various waysincluding chat, email, file sharing, blogging, and affinity groups.Affinity groups may be groups that people join for any number of reasonssuch as to express a particular interest, engage in discussions about aparticular subject or express a relationship with other members. Usersof social networks establish relationships with each other in variousways on the social networks, including by joining common affinitygroups, becoming “friends” or making a “connection.”

A person using a user device (e.g., 140, or 150) may log into a socialnetwork (e.g., 120 or 130) to interact and establish relationships withother users. In some cases, the same person may access the socialnetworks using different user devices at different times. For example,the person may use their desktop computer when at work, their laptopcomputer in a meeting, and their mobile phone when on the road. In eachcase, the person is identified to the social network by using a useridentification to log into the social network from one of the userdevices. In some cases, the user may use the user device 140 toconcurrently log into both the social network 120 and the social network130.

A first person may use the user device 140 to connect through theinternet 110 to the social network 120. A second person may use the userdevice 150 to connect through the internet 110 to the social network120. The first person may interact and become friends with the secondperson on the social network 120. Similarly, the first and second personmay also interact and become friends on the social network 130.

In some embodiments, users log into a social network or their userdevice using a user identification and password. In some cases, the useris somewhat anonymous in that the user identification is a user name orscreen name. In other cases, the user identification may be associatedwith more specific identifying information, such as the user's real nameor social security number. In some embodiments, a person is identifiedby different user names depending on which social network or user devicethey are accessing. In such embodiments, the search device may associatea common user identification with each of the user names so thatinformation associated with this person may be aggregated acrossmultiple information sources.

The first person may click on a widget in a web page on the socialnetwork 120 to indicate that the user prefers or recommends a firstbusiness. The widget is a mechanism for allowing the user to interactwith the social network 120. For example, the widget may be a button,checkbox, or drop down list in a web page on the social network 120. Thesecond person may click a widget in a web page on the social network 120that indicates that the user prefers or recommends the first business aswell. The second person may recommend a second business instead of or inaddition to the first business. Similarly, other users may indicatepreferences for one or more businesses on the social network 120 and thesocial network 130. Furthermore, these users may interact similarly withother social networks.

The context and processes for indicating these preferences andrecommendations may be implemented in any of a number of ways on each ofthese social networks. For example, a user may browse a list ofbusinesses from a database and click on a widget associated with aparticular business to indicate a preference. In other cases, a user mayreview the recommended businesses of other users and click on a widgetto adopt a particular recommendation as their own.

In some cases, a user may log into an account managed by the searchdevice 100 using a user identification and make recommendations directlyassociated with the user profile maintained by the search device 100.For example, the user may visit the web site of a business and click ona button on their browser to add that business to their recommendedbusinesses as stored in their account on the search device. In othercases, the user logs into the social network using a user identificationand makes the recommendations within the context of that social network.The search device 100 subsequently retrieves this information from thesocial network and associates the information with the user profilemaintained by the search device.

In some embodiments, users of the social networks recommend businessesby clicking on a widget associated with a particular business toindicate that the user prefers or recommends that business. Theserecommendations and preferences may be available to other users invarious ways including by viewing the recommending user's profile on thesocial network or receiving announcements of new recommendations througha messaging feature of the social network. The social network may employother methods of indicating and sharing a preference or recommendationfor certain businesses.

In some embodiments, users select one of several levels of favorites toindicate the strength of a recommendation. For example, user may clickon one of two buttons to indicate that a particular business is either“recommended” or “highly recommended.” In other cases, a multiple pointscale may be used to indicate the strength of the recommendation. Othermethods for users to indicate a preference for a certain business may beused. In some embodiments, these recommended businesses may be listed inthe favorite list in the profile of the user on the social network orinternet site

In the illustrated embodiment, the search device 100 is coupled throughthe internet 110 to the social network 120 and the social network 130.The search device 100 includes one or more computers configured toperform the functionality described herein. For example, the searchdevice may include one or more computers to implement web serverfunctionality to interface with the user devices 140 and 150 and socialnetworks 120 and 130 over the internet. Furthermore, the search devicemay include one or more databases configured to manage informationincluding user profiles and recommended businesses and configured tosearch that information in response to search queries. The databases areconfigured to store multiple relationships between user identifications,a list of favorite businesses of each user identification, andrelationships between each user identification and an affinity group orsocial network.

The search device 100 interacts with the social networks 120 and 130 andaggregates recommended businesses and user identifications of peoplethat recommend each recommended business on the social networks. Thesearch device 100 also retrieves one or more relationship graphs (e.g.,210 illustrated in FIG. 2) from the social network 120 and the socialnetwork 130. The relationship graphs include information about therelationships between user identifications on the social networks. Forexample, the relationship graph may include information about whichusers are members of each affinity groups, which users are directlyrelated as friends or connections, and which users are more remotelyconnected through intermediate friends or connections, such as a friendof a friend.

In some cases, the user identifications are the login identifier foreach social network. The social network 110 may maintain a relationshipgraph indicating the relationships of each user based on a unique useridentification (for example, login id) for those users on the socialnetwork 110 and the social network 120 may maintain a relationship graphindicating the relationships of each user based on the unique useridentification for those users on the social network 120. In many cases,a particular person's login id for one social network will not be thesame on another social network. In some embodiments, the useridentifications known to be associated with the same person (aliases) indifferent social networks are associated each other to allow therelationship graphs from several social networks to be merged at leastpartially based on the known aliases. In some cases, the search device100 independently uses each relationship graph for each social networkbased on the internally unique user identifications within each socialnetwork. In other cases, the search device 100 uses a mergedrelationship graph based on the internally unique user identificationswithin each social network and aliases across social networks.

The user device 140 and the user device 150 are also connected over theinternet 110 to the search device 100. Persons can submit businesssearch queries on their user devices. For example, a person wanting tofind someone to fix their Porche, may submit a search query “Porschemechanic” to the user device 140 using a keyboard or voice input, forexample. The user device 140 submits the search query over the internet110 to the search device 100. In some cases, a location of the searchinguser is provided to the search device 140 so that the search service canprioritize relevant businesses based at least in part by distance fromthe searching user. The search device 100 provides the search resultsover the internet to the user device 140 to the searching user.

In some embodiments, the search device 100 selects businesses inresponse to a search query based at least in part on the number ofrecommendations of each business by recommending users of a certaindegree of closeness to the searching user in the one or morerelationship graphs. For example, the search device 100 may select andrank businesses based at least in part on the number of recommendationsfrom recommending users with a direct relationship with the searchinguser. In other cases, the search device 100 counts users withrecommendations from recommending users with second degree, third degreeand higher degree relationships with the searching user and ranks thebusinesses at least in part based on these counts.

In some embodiments, the search device 100 counts only recommendationsfrom users in certain affinity groups or particular social networks. Forexample, if the searching user is searching for a financial advisor, thesearching user might only be interested in the recommendations of peoplein a selected affinity group. In some cases, the user specifies aparticular affinity group or social network to include based for exampleon their perception of the relevant expertise of members of that groupor network. In other cases, the selection of groups or networks toinclude is done automatically based on the search query. For example,the search device 100 may only count relationships within financerelated groups if the search device 100 determines that the search queryis related to a finance related matter.

In some embodiments, the search device 100 transmits the search resultsover the internet 110 to the user device 140. The user device 140presents the search results with an indication of one or more numbers ofrecommendations according to one or more criteria. In some cases, theone or more number of recommendations may include the number ofrecommendations overall. In some cases, one or more numbers ofrecommendations are presented for one or more particular categories,such as number of recommendations among each of the searching user'sfirst degree relationships, second degree relationships, and thirddegree relationships. Other categories may include the number ofrecommending users that are a member of a particular affinity group or amember of a particular social network.

The user device 140 also presents the search results with an indicationof one or more user identifications associated with recommendations. Insome cases, all the user identifications associated with the recommendedbusiness are presented. The search results may include useridentifications selected or prioritized based on the recommending user'srelevance to the searching user. In some embodiments, the most relevantusers may be recommending users with the closest relationships to thesearching user. The closest relationships are ones which the searchinguser may recognize and find most persuasive in influencing theirselection of one of the presented businesses. In some embodiments, therelevance of recommending users may be based on one or more factorsincluding the degree of closeness of the relationship between the twouser identifications, the number of independent relationships connectingthe two user identifications, the number of common group memberships,and the relevance of each affinity group to the search query.

In some embodiments, the search query is submitted to the search deviceby an application in a social network. For example, users of a socialnetwork may load a social network application to provide for businesssearch queries in their social network profile, provide a mechanism forthe user to identify and share favorite businesses on that socialnetwork and also allow the search device to access that user's favoritebusinesses and relationships on that social network.

In other embodiments, the search query is submitted through anapplication programming interface (API) to the search device. Forexample, the API may provide for business search queries in their socialnetwork profile, provide a mechanism for the user to identify and sharefavorite businesses on that website and also allow the search device toaccess that user's favorite businesses and relationships on thatwebsite. In some cases, the search results not only includes therecommendations from recommending users in the network of the searchinguser, but also recommendations from recommending users associated withthe website incorporating the API.

For example, an API as incorporated in a newspaper website may allow thesearch device to aggregate the business recommendations of the newspaperwebsite users. The search device can then include the newspaper websiteuser recommendations in search results provided to the newspaperwebsite. A product review website may incorporate the API to include therecommendations of users of the product review website.

In some embodiments, the search device 100 aggregates recommendations ofall users of the website. In other embodiments, the API can beconfigured to specify that recommendations from a specific subset ofusers on the website are included. For example, the search device 100might aggregate the recommendations of newspaper editors on thenewspaper website and aggregate the recommendations of product reviewstaff on the product review website.

In some cases, the recommendations of these the users associated withthe website are incorporated into the search results on the website forall searches. In other embodiments, the recommendations of these us areonly incorporated into certain types of searches, such as search queriesrelated to their area of expertise. For example, only restaurantrecommendations of the restaurant critics on the newspaper website mightbe incorporated into the search results.

FIG. 2 shows a graphical representation of one embodiment of tworelationship graphs. The relationship graphs are described withreference to FIG. 1.

A graph 210 represents a portion of the relationship information thatthe search device 100 receives from the social network 120 and a graph220 represents a portion of the relationship that the search enginereceives from the social network 130. The relationship graphs aresimplified in order to more clearly illustrate the concept. In someembodiments, there are millions of user identifications interconnectedin each relationship graph.

On the graph 210, a user identification (UI) 201 is connected to a UI203 and the UI 203 is connected to a UI 202. In some embodiments, theseconnections may be established in response to people associated with apair of user identifications becoming “friends,” making a connection orjoining a common affinity group, for example, on the social network 120.In some embodiments, the connections between user identifications on thegraphs include information about the types and number of relationshipsbetween the corresponding user identifications.

On the graph 220, a UI 208, being connected to a UI 207 directly, has afirst degree relationship with the UI 207. The UI 208, being connectedto a UI 206 through the UI 207, has a second degree relationship withthe UI 206. The UI 208, being connected to a UI 204 through the UI 207and the UI 206, has a third degree relationship with a UI 204. Higherorder relationships between two user identifications include additionalintermediate relationships between the user identifications.

In some embodiments, the search device 100 independently uses therelationship graphs (e.g., 210, 220) received from each social network(e.g., 120, 130) to carry out a method as described herein. In otherembodiments, the search device 100 merges the relationship graphs fromdifferent social networks (e.g., 120, 130) based on aliases of useridentifications provided to the search device 100. More than one useridentification (aliases) may be associated with the same person on oneor more social networks. For example, a person may use the UI 203 forthe social network 120 and the UI 204 for the social network 130. Whenthe person registers with the search device 100, the person may indicatethe user identifications the person uses on each of one or more socialnetworks (e.g., 120, 130). The search device 100 may create a UI 205 toassociate with or replace both the UI 203 and the UI 204 in therelationship graph used by the search device 100. In addition, thesearch device 100 may merge the graph 210 and the graph 220 byassociating user identifications that the search device 100 determinesis associated with the same person. For example, if the search devicedetermines that UI 203 is associated with the same person as UI 204,then the graph 210 and the graph 220 can be merged by combining UI 203and UI 204 into UI 205. This merged graph can lead to additionalrelationships between graphs. For example, if graph 210 is merged withgraph 220 at UI 205, UI 206 has a second degree relationship with UI 201even though the user identifications are initially received fromdifferent social networks.

In some embodiments, the search device 100 may receive only a portion ofthe relationship information available to the social networks due toprivacy controls and other access restrictions for each social network.For example, the social network 120 may only allow the search device 100to retrieve the direct relationships (e.g., friends list) of registeredusers that grant access to the search device 100. In other cases, thesocial network grants access to user identifications of second, thirdand higher degree relationships of the registered users. Users of thesocial network may become a registered user of the search device 100 andgrant access to the search device 100 by loading an application on thesocial network 120 that enables such access.

In some embodiments, registered users submit profile information ontheir user device and the user device submits that profile informationto the search device 100. The profile information may include one ormore user identifications associated with that person on one or moresocial networks. The search device 100 may use that information toaccess the portions of the relationship graphs available for that userfrom the social networks and merge the relationship graphs from two ormore social networks based on aliases for that person across the socialnetworks (e.g., 120, 130).

In some embodiments, the social network (e.g., 120 or 130) and theapplication allow for the users to control what information is providedto the search device 100. Each social network may have independentrestrictions that control accessibility of the relationship informationby the search device 100. Furthermore, the search device 100 may notmerge user identifications across different social graphs because thesearch device 100 may not receive information about all the useridentifications that are associated with the same person.

In other embodiments, the relationship graphs may be represented inother ways and incorporate other relationship information.

FIG. 3 shows a flow chart of one embodiment of a search method. In someembodiments, the process is implemented in a system as illustrated inFIG. 1. However other general or special purpose apparatus and systemsmay be used to carry out a process contemplated to be within the spiritand scope of this disclosure.

In process 300, a search device 110 aggregates preferred or recommendedbusinesses and associated user identifications of the personsrecommending each business. In one embodiment, the search device 110 iscommunicatively coupled to one or more sources of preferred orrecommended businesses, such as a social network (e.g., 120 or 130) or abrowser running on a user device (e.g., 140 or 150).

In some embodiments, the search device 110 aggregates favoritebusinesses and associated user identifications by interfacing with thesocial networks (e.g., 120 or 130) through an application programminginterface (API). In some embodiments, the search device aggregatesfavorite businesses and associated user identifications by interfacingwith a user device running a browser with a browser plug-in. A user mayvisit a website of a business using the browser and click on a widget toidentify that business as a favorite business. In some embodiments, theuser device (e.g., 140 or 150) transmits the business and a useridentification to the search device (100). In other embodiments, theuser device saves that favorite business locally on the user device tobe retrieved by the search device at a later time. In some embodiments,the user identification is the login identification specified by logginginto the search device using the browser plug in.

In other embodiments, the search device 100 aggregates favoritebusinesses and associated user identifications by screen scraping, dataextraction and other methods of mining information from sources such asdata files and web pages.

In process 310, the search device 100 receives one or more relationshipgraphs (e.g., 210, 220) to indicate the relationship between useridentifications. The various social networks (e.g., 120, 130) and othersources of preferred or recommended businesses are accessed to receive arelationship graph indicating the relationships between the useridentifications on that social network or other source of preferred orrecommended businesses.

In some embodiments, the relationship graph (e.g., 210, 220) received bythe search device 100 represents only a portion of the relationshipinformation on the social network (e.g., 120 or 130) due to privacyrestrictions limiting access to some user identifications andrelationships on that social network. In some embodiments, the searchdevice may only have access to the user identifications most closelyconnected to users granting access to the search device. Furthermore, insome embodiments, the degree of accessibility of user identificationsassociated with a specific user identification may depend on privacyconfiguration settings for that specific user. In addition, a useridentification can be association with different user names for each ofseveral sources of favorite businesses so that the relationship graphsfor each of those several sources can be merged.

The user identifications may be user names, screen names, real names, orsocial security numbers, for example. In some embodiments, relationshipsare established by being members of a common affinity group. Forexample, the relationship graph may indicate two user identificationsare associated with each other as members of a common FACEBOOK group.Furthermore, the relationship graph may indicate two useridentifications are associated with each other because they establisheda connection with each other on Linkedin or became “friends” with eachother on MySpace. In some cases, the relationship graph may indicaterelationships of two or more degrees by tracking user identificationrelationships through one or more intermediate user identifications.

In process 320, the search device 100 searches one or more databases toselect businesses based on the associated user identification and theone or more relationship graphs. In one embodiment, the search devicereceives a search query and selects businesses most relevant to thesearch query based on keyword matching, semantic matching, popularityranking, concept matching or other methods of using a search query toselect businesses.

In some embodiments, the database contains only preferred or recommendedbusinesses having at least one associated user identification. Eachassociated user identification corresponds to a person that indicated apreference or recommendation for the corresponding business. In otherembodiments, the database also includes businesses that do not have anassociated user identification.

In some embodiments, the search device 100 selects businesses at leastin part based on the number of associated user identifications in thedatabase. In some cases, the search device selects only businesses witha predetermined minimum number of associated user identifications or thehighest number of user identifications in the database. Alternatively,the search device selects 100 only the most relevant relationships tothe searching user. For example, the search device may count thoseassociated user identifications within a predetermined maximum degree ofcloseness between the user identification of the recommending users andthe searching users.

In some cases, closeness may also be based on the number of independentrelationships between two users. For example, two users may be directlyrelated though a friendship. These two users may be considered to have acloser relationship when they are also members of the same group ascompared to two users that do not share a common group. Furthermore twousers that share multiple common groups and have friendships orconnections on multiple sites or social networks may be considered to becloser than those that have fewer connections.

In some cases, the search device 100 selects the businesses using adatabase query that incorporates a search string related to identifyingrelevant businesses, the associated user identifications to indicate thenumber of recommendations and the relationship of the recommending usersto the searching user according to one or more relationship graphs. Inother embodiments, the search device selects the businesses used two ormore steps including a database search to identify relevant businessesand one or more filtering processes to extract only those businessesthat meet certain other qualifications. These qualifications may includehaving a minimum number of user recommendations overall, having aminimum number of recommendations from recommending users within amaximum degree of closeness to the searching user, and having a minimumnumber of recommendations from recommendations within one or moreselected affinity groups. Other single or multi-step processes may beused to identify selected businesses may be used.

In process 330, the search device 100 ranks the selected businessesbased on the associated user identification and the one or morerelationship graphs. In one embodiment, the search device receives asearch query and selects businesses most relevant to the query and ranksthem according to the number of associated user identifications within amaximum degree of closeness with the searching user.

In process 340, the search device 100 provides the search resultincluding one or more selected businesses and associated useridentifications in the ranked order. In one embodiment, the searchdevice provides the search result over a network, such as the internet,to the user device of the searching user. In other embodiments, the userdevice provides the one or more selected businesses and associated useridentifications in ranked order to a display. In yet other embodiments,the user device provides the one or more selected businesses andassociated user identifications in ranked order to a text-to-speechdevice to present the information in audio form. Other methods ofproviding the search result to a searching user may be used.

FIG. 4 illustrates a screen display of a business search interfaceaccording to one embodiment. The screen display represents a display ofbusiness search interface on a computer, PDA, or mobile telephone, forexample.

A search query 400 is submitted, by a user device (e.g., 140 or 150) toa search device 100, in a field on the display. In some embodiments, thesearch query 400 includes a text string like “coffee” to be used toidentify businesses relevant to the searching user's need or want. Thesearch device may use the search query 400 for keyword matching,semantic matching, concept matching or other ways to identify the mostrelevant businesses within a database. For example, “coffee shops” maybe identified through simply matching the keyword “coffee” from thesearch query with the names and descriptions of businesses in thedatabase. In some cases, synonyms may be recognized such as “java” andin other cases more sophisticated search techniques are used.

In some embodiments, the search query 400 also includes a location, suchas “San Francisco, Calif.” or “SF, Calif.”. The location is used toselect or prioritize businesses in part based on how close the businessis to this location. In other embodiments, a default location from thesearching user's profile is used when a location is not specified in thesearch query. In yet other embodiments, the location of the user device(e.g., 140 or 150) is submitted with the search query. The location ofthe user device may be automatically determined using a globalpositioning system (GPS) or other position determining apparatusintegrated into or communicatively coupled with the user device.

A tag cloud 405 displays tags related to the search query 400. The tagcloud 405 may include related search terms most frequently used inprevious search queries that included one or more terms in the searchquery 400. The size of the related search term may relate to therelative frequency of that term in previous search queries using atleast one term in the search query 400. In one embodiment, clicking on arelated search term in the tag cloud 405 modifies the search query 400to include the related search term. A new tag cloud may be producedbased on the modified search query.

A selector 410 and a selector 420 is a widget on the page that allowsthe user to select which categories of recommendations should be countedto present a ranked list of recommended businesses.

If selector 410 is clicked, all recommendations in the searching user'snetwork are counted. In one embodiment, the user's network includes allrecommending users having a first, second or third degree relationshipwith the searching user in one or more relationship graphs. In otherembodiments, the user's network may be more or less restrictive in termsof the closeness of the relationships included. The user's network maybe defined in other ways. If selector 420 is clicked, allrecommendations in the entire network are counted. In one embodiment,the entire network may include all recommendations by all recommendingusers as aggregated by the search device. Additional selectors may beused to predefine different categories of recommendations to count todetermine the ranking of selected businesses.

A business 420 includes information associated with a recommendedbusiness listed first in the ranked list of businesses and a business425 includes information associated with a recommended business listedsecond in the ranked list of businesses. The information associated witheach business includes the business name, address, phone number,universal resource locator (URL), email address and other details. Graphcounts 430, content 440 and widgets 451, 452, 453 and 454 are associatedwith the business 420 in the ranked list. Graph counts 435, content 445and widgets 461, 462, 463 and 464 are associated with the business 425in the ranked list. Additional businesses are listed in the ranked listof businesses.

In the illustrated embodiment, the graph counts identify the number ofrecommending users with one degree relationships, with one or two degreerelationships, and with one, two or three degree relationships with thesearching user. For example, graph counts 430 indicates that thebusiness 420 has 361 recommending users that have first degree (direct)relationships with the searching user, 500+ recommending users that havefirst or second degree relationships with the searching user and 3000+recommending users that have first, second or third degree relationshipswith the searching user.

Content may be associated with the recommended business that isretrieved from the social network or other sources. This content may beuser submitted reviews, videos, photos and other information. In somecases, the content may be submitted or otherwise associated with one ofthe recommending users. For example, the content 440 may be submitted byrecommended users counted in the graph 430 and associated with thebusiness 420.

Widget 451 allows the user to add the business 420 as a recommendedbusiness associated with their user identification. In some cases, theuser identification is one associated with the users profile on thesearch device. Widget 452 sends the business 420 to their mobiletelephone using SMS messaging, for example. Widget 454 sends thebusiness 420 to their email address. Widget 453 sends the business 420to their friends as determined by the one or more relationship graphs.

FIG. 5 illustrates a screen display of a business search interfaceaccording to another embodiment.

The graph counts 530 displays some or all of the user identifications ofrecommending users that recommended the business identified in abusiness 520. In some embodiments, the businesses are ranked by one ormore of the different categories of user identifications as describedwith reference to FIG. 4. In some embodiments, the graph count 530includes user identifications of some or all of the recommending usersfor the corresponding business. In some embodiments, only a portion ofthe user identifications counted in the ranking of each business islisted. In one embodiment, the search device selects which useridentifications to present based on the closeness of the relationship tothe searching user using on one or more relationship graphs.

The searching user may be influenced to choose a specific one of theselected businesses based on its rank position but also based on thecredibility that the searching user attributes to the users indicated bythe displayed user identifications. Although the business 525 is rankedbelow the business 520, the user may attribute more credibility to therecommendation of the associated user identifications in the graphcounts 530 than the recommendation of the associated useridentifications in the graph counts 535. The searching user may chose alower ranked selected business over a higher ranked selected businessbased on the identity of one or more recommending users.

FIG. 6 is a diagrammatic representation of an embodiment of a machine600, within which a set of instructions for causing the machine toperform one or more of the methodologies discussed herein may beexecuted. The machine may be connected (e.g., networked) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In one embodiment, the machine communicates withthe server to facilitate operations of the server and/or to access theoperations of the server.

In some embodiments, the machine 600 is a search device 100 according toan embodiment as described herein. In other embodiments, the machine 600is a component of the search device 100, such as one or more computerswithin the search device 100. In other embodiments, the machine 600 is auser device (e.g., 140, 150) according to an embodiment as describedherein. In one embodiment, the machine 600 is a portion of a socialnetwork (e.g., 120, or 130).

The machine 600 includes a processor 602 (e.g., a central processingunit (CPU) a graphics processing unit (GPU) or both), a main memory 604and a nonvolatile memory 606, which communicate with each other via abus 608. In some embodiments, the machine 600 may be a desktop computer,a laptop computer, personal digital assistant (PDA) or mobile phone, forexample. In one embodiment, the machine 600 also includes a videodisplay 610, an alphanumeric input device 612 (e.g., a keyboard), acursor control device 614 (e.g., a mouse), a drive unit 616, a signalgeneration device 618 (e.g., a speaker) and a network interface device620.

In one embodiment, the video display 610 includes a touch sensitivescreen for user input. In one embodiment, the touch sensitive screen isused instead of a keyboard and mouse. The disk drive unit 616 includes amachine-readable medium 622 on which is stored one or more sets ofinstructions 624 (e.g., software) embodying any one or more of themethodologies or functions described herein. The instructions 624 mayalso reside, completely or at least partially, within the main memory604 and/or within the processor 602 during execution thereof by thecomputer system 600, the main memory 604 and the processor 602 alsoincluding machine-readable media. The instructions 624 may further betransmitted or received over a network 640 via the network interfacedevice 620. In some embodiments, the machine-readable medium 622 alsoincludes a database 625 including the aggregated businesses, associateduser identifications and relationship graphs.

While the machine-readable medium 622 is shown in an exemplaryembodiment to be a single medium, the term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” shall also be taken to include any medium thatis capable of storing, encoding or carrying a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, optical and magnetic media, andcarrier wave signals.

In general, the routines executed to implement the embodiments of thedisclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “programs.” For example, one or moreprograms may be used to execute specific processes described herein. Theprograms typically comprise one or more instructions set at varioustimes in various memory and storage devices in the machine, and that,when read and executed by one or more processors, cause the machine toperform operations to execute elements involving the various aspects ofthe disclosure.

Moreover, while embodiments have been described in the context of fullymachines, those skilled in the art will appreciate that the variousembodiments are capable of being distributed as a program product in avariety of forms, and that the disclosure applies equally regardless ofthe particular type of machine or computer-readable media used toactually effect the distribution. Examples of machine-readable mediainclude but are not limited to recordable type media such as volatileand non-volatile memory devices, floppy and other removable disks, harddisk drives, optical disks (e.g., Compact Disk Read-Only Memory (CDROMS), Digital Versatile Disks, (DVDs), etc.), among others, andtransmission type media such as digital and analog communication links.

Although embodiments have been described with reference to specificexemplary embodiments, it will be evident that the various modificationand changes can be made to these embodiments. Accordingly, thespecification and drawings are to be regarded in an illustrative senserather than in a restrictive sense. The foregoing specification providesa description with reference to specific exemplary embodiments. It willbe evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope as set forth in thefollowing claims. The specification and drawings are, accordingly, to beregarded in an illustrative sense rather than a restrictive sense.

1. A method comprising: aggregating businesses and associated useridentifications into a database; receiving one or more relationshipgraphs, the relationship graphs comprising relationships between theuser identifications; searching the database; and providing one or moreselected businesses and one or more associated user identifications fromthe database.
 2. The method of claim 1 wherein the businesses andassociated user identifications are aggregated from at least one of abrowser and a social network.
 3. The method of claim 1 wherein the useridentifications comprise a user name.
 4. The method of claim 1 whereinthe relationships between the user identifications comprise at least oneof relationships between user identifications in one or more socialnetworks and relationships between user identifications in one or moreaffinity groups.
 5. The method of claim 4 wherein the one or moreaffinity groups are selected based on a user query.
 6. The method ofclaim 1 wherein searching the database comprises selecting one or moreselected business based at least in part on a search query.
 7. Themethod of claim 1 wherein searching the database comprises selecting theone or more selected businesses based on the number of useridentifications associated with each of the selected businesses in thedatabase.
 8. The method of claim 1 wherein searching the databasecomprises selecting the one or more selected businesses based on thenumber of related user identifications associated with each of theselected businesses in the database, the related user identificationssatisfying a criteria based on the one or more relationship graphs. 9.The method of claim 8 wherein the criteria comprises at least one of aspecified user having a relationship within a predetermined number ofdegrees with the related user identifications and the specified userbeing in one or more common affinity groups with each of the relateduser identifications.
 10. A machine-readable medium that providesinstructions for a processor, which when executed by the processor causethe processor to perform a method comprising: aggregating businesses andassociated user identifications into a database; receiving one or morerelationship graphs, the relationship graphs comprising relationshipsbetween the user identifications; searching the database; and providingone or more selected businesses and one or more associated useridentifications from the database.
 11. The machine readable medium ofclaim 10 wherein the businesses and associated user identifications areaggregated from at least one of a browser and a social network.
 12. Themachine readable medium of claim 10 wherein the user identificationscomprise a user name.
 13. The machine readable medium of claim 10wherein the relationships between the user identifications comprise atleast one of relationships between user identifications in one or moresocial networks and relationships between user identifications in one ormore affinity groups.
 14. The machine readable medium of claim 10wherein the one or more common affinity groups are selected based on oneor more relationships to a user query.
 15. The machine readable mediumof claim 10 wherein searching the database comprises selecting one ormore selected businesses based at least in part on a search query. 16.The machine readable medium of claim 10 wherein searching the databasecomprises selecting the one or more selected businesses based on thenumber of user identifications associated with each of the selectedbusinesses in the database.
 17. The machine readable medium of claim 10wherein searching the database comprises selecting the one or moreselected businesses based on the number of related user identificationsassociated with each of the selected businesses in the database, therelated user identifications satisfying a criteria based on the one ormore relationship graphs.
 18. The machine readable medium of claim 17wherein the criteria comprises at least one of a specified user having arelationship within a predetermined number of degrees with the relateduser identifications and the user being in one or more common affinitygroups with each of the related user identifications.
 19. An apparatuscomprising: a network interface configured to interface with a network;a database coupled to the network interface and configured to aggregatebusinesses, user identifications associated with the businesses, andrelationships between the user identifications; a processor configuredto receive a search query and search the database; and present one ormore selected businesses and one or more associated user identificationsbased at least in part on the relationships between useridentifications.
 20. The apparatus of claim 19 wherein the networkinterface is configured to be communicatively coupled to a user deviceand a social network.